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arXiv · 2606.11320

Semantic Segmentation of Node and Edge Diagrams for Assistive Technology

Michael Cormier·Yichun Zhao·Laura Paul·Cameron Swift·Duc Tri Dang·Miguel Nacenta
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Abstract

In this paper, we present a novel set of related models for semantic segmentation of node-link diagrams. These diagrams are frequently used to represent mathematical graphs, relationships between concepts, and flowcharts. Such diagrams are difficult to access non-visually; while some assistive interfaces have been designed for node-link diagrams, they rely upon a machine-readable representation of the diagram, whereas such diagrams will generally be made available as bitmap images. Our compact deep learning models show excellent quantitative and qualitative performance on a large synthetic dataset of node-link diagrams, reaching per-pixel accuracy over 93\%.

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Michael Cormier, Yichun Zhao, Laura Paul, Cameron Swift, Duc Tri Dang, Miguel Nacenta. 2026-06-09. Semantic Segmentation of Node and Edge Diagrams for Assistive Technology. https://arxiv.org/abs/2606.11320

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